Incomplete Multi-view Classification via Discriminative and Sparse Representation

Generally, the traditional multi-view learning methods assume that all samples are completed in all views. However, this assumption often fails in real applications because of limited access to data, equipment malfunc-tion, as well as occlusion and so on. Thus, it is ineffective to directly use these traditional methods for addressing incomplete multi-view data. At present, several effective incomplete learning algorithms have been proposed, but they do not make full use of label information and thus reduce the discrimination of the recovered samples. Therefore, this paper proposes an incomplete multi-view classification method via discriminative and sparse representation (IMVC-DSR). Specifically, this method is based on the assumption that missing samples can be represented linearly and sparsely by a few observed samples. Meanwhile, in order to make full use of label prior information to improve the discrimination of recovered samples, this method encourages that missing samples are represented by the samples from their same classes rather than the others. Also, according to the view consistence across multi-view, this paper designs a selection operator to select the same samples from different views and meanwhile expects their linear representations are consistent with each other. Finally, experiments demonstrate the efficacy of the proposed method on five public benchmark datasets